Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation

Authors:
Shahrul Badariah Mat Sah;Vic Ciesielski;Daryl D'Souza;Marsha Berry
Affiliations:
School of Computer Science and Information Technology, RMIT University, Melbourne, Australia 3001;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia 3001;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia 3001;School of Creative Media, RMIT University, Melbourne, Australia 3001
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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Abstract

Photomosaics are a new form of art in which smaller digital images (known as tiles) are used to construct larger images. Photomosaic generation not only creates interest in the digital arts area but has also attracted interest in the area of evolutionary computing. The photomosaic generation process may be viewed as an arrangement optimisation problem, for a given set of tiles and suitable target to be solved using evolutionary computing. In this paper we assess two methods used to represent photomosaics, genetic algorithms (GAs) and genetic programming (GP), in terms of their flexibility and efficiency. Our results show that although both approaches sometimes use the same computational effort, GP is capable of generating finer photomosaics in fewer generations. In conclusion, we found that the GP representation is richer than the GA representation and offers additional flexibility for future photomosaics generation.